Skip to content
Archive of posts filed under the Causal Inference category.

American Causal Inference May 2020 Austin Texas

Carlos Carvalho writes: The ACIC 2020 website is now up and registration is open. As a reminder, proposals information can be found in the front page of the website. Deadline for submissions is February 7th. I think that we organized the very first conference in this series here at Columbia, many years ago!

Will decentralised collaboration increase the robustness of scientific findings in biomedical research? Some data and some causal questions.

Mark Tuttle points to this press release, “Decentralising science may lead to more reliable results: Analysis of data on tens of thousands of drug-gene interactions suggests that decentralised collaboration will increase the robustness of scientific findings in biomedical research,” and writes: In my [Tuttle’s] opinion, the explanation is more likely to be sociological – group […]

No, I don’t think that this study offers good evidence that installing air filters in classrooms has surprisingly large educational benefits.

In a news article on Vox, entitled “Installing air filters in classrooms has surprisingly large educational benefits,” Matthew Yglesias writes: An emergency situation that turned out to be mostly a false alarm led a lot of schools in Los Angeles to install air filters, and something strange happened: Test scores went up. By a lot. […]

The Generalizer

I just saw Beth Tipton speak at the Institute of Education Sciences meeting on The Generalizer, a tool that she and her colleagues developed for designing education studies with the goal of getting inferences for the population. It’s basically MRP, but what is innovative here is the application of these ideas at the design stage. […]

DAGS in Stan

Macartan Humphries writes: As part of a project with Alan Jacobs we have put together a package that makes it easy to define, update, and query DAG-type causal models over binary nodes. We have a draft guide and illustrations here. Now I know that you don’t care much for the DAG approach BUT this is […]

External vs. internal validity of causal inference from natural experiments: The example of charter school lottery studies

Alex Hoffman writes: I recently was discussing/arguing about the value of charter schools lottery studies. I suggested that their validity was questionable because of all the data that they ignore. (1) They ignore all charter schools (and their students) that are not so oversubscribed that they need to use lotteries for admission. (2) They ignore […]

Causal inference, adjusting for 300 pre-treatment predictors

Linda Seebach points to this post by Scott Alexander and writes: A recent paper on increased risk of death from all causes (huge sample size) found none; it controlled for some 300 cofounders. Much previous research, also with large (though much smaller) sample sizes found very large increased risk, but used under 20 confounders. This […]

Causal inference and within/between person comparisons

There’s a meta-principle of mathematics that goes as follows. Any system of logic can be written in various different ways that are mathematically equivalent but can have different real-world implications, for two reasons: first, because different formulations can be more directly applied in different settings or are just more understandable by different people; second, because […]

“Machine Learning Under a Modern Optimization Lens” Under a Bayesian Lens

I (Yuling) read this new book Machine Learning Under a Modern Optimization Lens (by Dimitris Bertsimas and Jack Dunn) after I grabbed it from Andrew’s desk. Apparently machine learning is now such a wide-ranging area that we have to access it through some sub-manifold so as to evade dimension curse, and it is the same […]

What’s the evidence on the effectiveness of psychotherapy?

Kyle Dirck points us to this article by John Sakaluk, Robyn Kilshaw, Alexander Williams, and Kathleen Rhyner in the Journal of Abnormal Psychology, which begins: Empirically supported treatments (or therapies; ESTs) are the gold standard in therapeutic interventions for psychopathology. Based on a set of methodological and statistical criteria, the APA [American Psychological Association] has […]

My talk at Yale this Thursday

It’s the Quantitative Research Methods Workshop, 12:00-1:15 p.m. in Room A002 at ISPS, 77 Prospect Street Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University It is not always clear how to adjust for control data in causal inference, […]

What happens to your metabolism when you eat ultra-processed foods?

Daniel Lakeland writes: Hey, you wanted examples of people doing real science for the blog! Here’s a randomized controlled trial with a within-subjects crossover design, and completely controlled and monitored conditions, in which all food eaten by the subjects was created by the experimenters and measured carefully, and the participants spent several weeks in a […]

“Causal Processes in Psychology Are Heterogeneous”

Niall Bolger sends along this article he wrote with Katherine Zee, Maya Rossignac-Milon, and Ran Hassin, which begins: All experimenters know that human and animal subjects do not respond uniformly to experimental treatments. Yet theories and findings in experimental psychology either ignore this causal effect heterogeneity or treat it as uninteresting error. This is the […]

We’re hiring an econ postdoc!

It’s for hierarchical modeling for policy analysis in Stan. We’re really excited about this project. Will share more details soon, but wanted to get this out right away.

Are statistical nitpickers (e.g., Kaiser Fung and me) getting the way of progress or even serving the forces of evil?

As Ira Glass says, today we have a theme and some variations on this theme. Statistical nitpickers: Do they cause more harm than good? I’d like to think we cause more good than harm, but today I want to consider the counter-argument, that, even when we are correct on the technical merits, we statisticians should […]

Bank Shot

Tom Clark writes: I came across this paper and thought of you. You might be aware of some papers that have been published about the effect of military surplus equipment aid that is given to police departments. Some economists have claimed to find that it reduces crime. My coauthors and I thought the papers were […]

Challenge of A/B testing in the presence of network and spillover effects

Gaurav Sood writes: There is a fun problem that I recently discovered: Say that you are building a news recommender that lists which relevant news items in each person’s news feed. Say that your first version of the news recommender is a rules-based system that uses signals like how many people in your network have […]

Let’s try this again: It is nonsense to say that we don’t know whether a specific weather event was affected by climate change. It’s not just wrong, it’s nonsensical.

This post is by Phil Price, not Andrew. If you write something and a substantial number of well-intentioned readers misses your point, the problem is yours. Too many people misunderstood what I was sayinga few days ago in the post “There is no way to prove that [an extreme weather event] either was, or was […]

More golf putting, leading to a discussion of how prior information can be important for an out-of-sample prediction or causal inference problem, even if it’s not needed to fit existing data

Steve Stigler writes: I saw a piece on your blog about putting. It suggests to me that you do not play golf, or you would not think this was a model. Length is much more important than you indicate. I attach an old piece by a friend that is indeed the work of a golfer! […]

“There is no way to prove that [an extreme weather event] either was, or was not, affected by global warming.”

This post is by Phil, not Andrew. It’s hurricane season, which means it’s time to see the routine disclaimer that no single weather event can be attributed to global warming. There’s a sense in which that is true, and a sense in which it is very wrong. I’ll start by going way back to 2005. […]